3D Nonlinear Viscoelastic-Viscoplastic Model for Ramming Paste Used in a Hall-Héroult Cell
Bibliographic record
Abstract
Ramming paste is a carbonaceous porous material used in Hall-Héroult cells. It is baked in place under varying loads. To model the cell mechanical behavior during its lifespan, it was necessary to develop a constitutive law that included ramming paste creep behavior. A three-dimensional (3D) nonlinear viscoelastic-viscoplastic constitutive law was devised and developed to model the primary and secondary creep stages of baked paste. The model consisted of two parts (i.e., viscoelastic and viscoplastic). Each creep mechanism was based on the existence of a dissipative potential for the hydrostatic and deviatoric parts. Analytical solutions were presented for linear creep behavior. For the nonlinear case, the deviatoric part of the viscoelastic behavior could be obtained numerically, and all other parts analytically. Finally, model parameters were identified for paste baked and tested at different temperatures. A pattern search algorithm was used to optimize the model parameters. A comparison of the results gained from the model with experimental results showed that the devised model well represented the nonlinear viscoelastic-viscoplastic behavior of the paste baked at 250°C and tested at room temperature. In addition, the model was able to predict the qualitative creep behavior of the paste baked at 350, 560, and 1,000°C and tested at 300, 300, and 25°C, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".